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our real system we had no data for the PI3K loop and used methods described in this manuscript to determine if the loop should be included given the data. Thus, for the “perfect” model we generated no data for this loop in order to use the model as a test case for the model selection methods, as we used it as a test case for other methods. However, the perfect model is unenlightening in this respect because model selection methods in a sense tell us what we already know, i.e., that the loop was present when the data were generated and are hence necessary
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In our real system we had no data for the PI3K loop and used methods described in this manuscript to determine if the loop should be included given the data. Thus, for the “perfect” model we generated no data for this loop in order to use the model as a test case for the model selection methods, as we used it as a test case for other methods. However, the perfect model is unenlightening in this respect because model selection methods in a sense tell us what we already know, i.e., that the loop was present when the data were generated and are hence necessary.
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Our methods identify typical, but not all, members of the ensemble. New data outside the error bars may, in principle, be consistent with the existing model but restrict the parameters to a formerly insignificant subregion
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Our methods identify typical, but not all, members of the ensemble. New data outside the error bars may, in principle, be consistent with the existing model but restrict the parameters to a formerly insignificant subregion.
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The prior distribution is flat in the (Formula presented) and in the parameters (logs of the rate constants). Using such a deceptively simple prior (equivalent, at least for the rate constants, to a Jeffrys prior) can have more subtle and serious consequences than one might think. For more details, see the model selection section and references therein. The (Formula presented) are quadratic in the cost and can be integrated out of the partition function (see text); they play the role of uninteresting “nuisance parameters” in the Bayesian language
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The prior distribution is flat in the (Formula presented) and in the parameters (logs of the rate constants). Using such a deceptively simple prior (equivalent, at least for the rate constants, to a Jeffrys prior) can have more subtle and serious consequences than one might think. For more details, see the model selection section and references therein. The (Formula presented) are quadratic in the cost and can be integrated out of the partition function (see text); they play the role of uninteresting “nuisance parameters” in the Bayesian language.
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